Getting the right answer is only a small part of the grade
Good quality interpretation of your results is the name of the game
If you see something that looks unusual in your data (outlier, some unusual distribution type) - investigate it!
When explaining your results, say something interesting about them. Did it match your expectations? Why or why not?
Brief explanations that simply repeat what I can visually see myself will not receive a good score
On the other hand, filling the homework with pages of not very interesting description is not valuable either. The goal isn’t to write the most words, but find the most interesting things in the data.
You do not need to be an expert in art for a good score, but I will expect you to look up basic information, such as “what does art typically sell for?” and “what is a standard size for paintings”? and so on to help you understand and set expectations your data.
The information requested in the question prompts are only a starting point, if you find other interesting information along the way, please report that. You don’t need to look at the data forever but if there is obviously something else interesting in the data you should report it.
Technical
Make sure your graphs are produced using ggplot(), are well labeled, and are easy to read.
Make sure your tables are produced with the kable() function from the knitr package, are well labeled, and are easy to read. You can make your tables prettier with the kableExtra package.
Make sure you do not have anything rendered in your HTML file besides your results and, when asked for by a question, your code. That means no warnings, messages, or other output should appear in your final rendered HTML file.
Convert your HTML file to PDF using the Microsoft Print to PDF option in the Print menu (PC) or the PDF button option from the Print menu (Mac)
Make sure to accurately mark each page a question answer appears on when submitting on GradeScope.
Introduction
Question 1: Describing your data (10 points)
1a. Where is this data from?
For this dataset, describe the data according to the five Ws & how defined in the textbook Chapter 1.2. What are some possible problems with the who and what of the dataset?
Who: 37638 artworks are the who of the data.
What: 37638 artworks sold at $9.47 billion. Maximum $119.92 million and minimum $3. The problem is the gap may be too large.
Where: Artist’s country of origin
When: Sold time
Why: The goal of the project is to classify if an artwork by 7 famous artists will be sold for more than $20,000, and if an artwork from less known artists will be sold for more than $2,000.
How: Use OpenCV and Python Image Library (PIL) to quantify aesthetics and extract features like dominant color, mean brightness, face count, etc.
For the following variables, please list the variable type as defined in the textbook Chapter 1.3:
artist (categorical)
country (categorical)
yearOfBirth(quantitative when, a particular year)
name(categorical)
year(quantitative when, a particular year)
ageOfPaiting(quantitative when, a particular year)
price(quantitative, $)
material (categorical)
height (quantitative, inches/centimeters)
dominantColor(categorical)
Question 2: Displaying and describing the data (15 points)
For the moment, we are going to focus on paintings by Chinese artists. You can create a subset of your data using the filter() verb as you learned in the DataCamp lab.
2a. Filtering your data
Using the filter() verb as described in the DataCamp lab, make a subset of your data that only includes art from Chinese artists. Show the code you used to make the subset using the #| echo: true code block option.
artist country yearOfBirth yearOfDeath name year
1 A Lao Chinese 1920 NA Dance People 1990
2 A Lao Chinese 1920 NA Dance People 1981
3 A Lao Chinese 1920 NA Dance People 1985
4 A Lao Chinese 1920 NA Dance People 1977
5 A'erbai Chinese NA NA Lion NA
6 Ba Weizu Chinese 1744 1793 Calligraphy in Clerical Script 1793
ageOfPainting price material height width
1 35 1499 ink_and_color_on_paper 26.38 18.90
2 44 4686 Ink_and_Color_on_Paper 25.98 16.93
3 40 4070 ink_and_color_on_paper 26.38 17.72
4 48 2184 ink_and_color_on_paper 17.72 15.75
5 NA 5814 ink_and_color_on_paper 23.62 15.75
6 232 6410 A_set_of_four_hanging_scrolls,_ink_on_paper 52.17 10.24
link
1 http://artsalesindex.artinfo.com/asi/lots/5025640
2 http://artsalesindex.artinfo.com/asi/lots/5026919
3 http://artsalesindex.artinfo.com/asi/lots/4935428
4 http://artsalesindex.artinfo.com/asi/lots/4745570
5 http://artsalesindex.artinfo.com/asi/lots/4754593
6 http://artsalesindex.artinfo.com/asi/lots/3630030
source
1 http://artinfo-images-350.s3.amazonaws.com/asi2-110264/395.jpg
2 http://artinfo-images-350.s3.amazonaws.com/asi2-110288/113.jpg
3 http://artinfo-images-350.s3.amazonaws.com/asi2-109321/173.jpg
4 http://artinfo-images-350.s3.amazonaws.com/asi2-106927/686.jpg
5 http://artinfo-images-350.s3.amazonaws.com/asi2-106932/1693.jpg
6 http://artinfo-images-350.s3.amazonaws.com/missingImages/0886555/1324.jpg
dominantColor brightness ratioUniqueColors thresholdBlackPerc
1 whites 222 0.16 5.68
2 whites 219 0.12 6.21
3 whites 221 0.17 7.99
4 whites 209 0.18 9.64
5 yellows 170 0.05 9.59
6 grays 171 0.13 18.57
highbrightnessPerc lowbrightnessPerc CornerPer EdgePer FaceCount soldtime
1 0 4.29 1.18 6.44 0 <NA>
2 0 4.93 1.30 7.96 0 <NA>
3 0 5.91 0.88 6.96 0 <NA>
4 0 8.16 1.27 7.92 0 <NA>
5 0 3.17 3.30 6.91 0 <NA>
6 0 13.05 8.81 16.31 0 <NA>
2b. Investigating height
Using the Think-Show-Tell framework from the textbook (example on page 71), investigate the distribution of the height of the Chinese paintings
Note: for this question and all other Think sections in the homework, you do not need to report the W’s of the data (you have already completed this in Q1)
Think: I want to investigate the distribution of the height of the Chinese paintings. The data are some of the heights of the Chinese paintings in history. The the heights of the Chinese paintings are quantitative.
Show:
chinese_art <-read.csv("artdata.cleaned.csv") %>%filter(country =="Chinese")ggplot(chinese_art, aes(x = height)) +geom_histogram(binwidth =10,color ="black") +labs(title ="Height of the Chinese Paintings, Histogram",x ="Height",y ="Count" ) +theme_minimal()
Warning: Removed 71 rows containing non-finite outside the scale range
(`stat_bin()`).
Tell: The mean an median are not close, so the outlier should be considered. Most height is under 100. The number of big paintings are relative small.
2c. Investigating width
Using the Think-Show-Tell framework from the textbook, investigate the distribution of the width of the Chinese paintings
Think: I want to investigate the distribution of the width of the Chinese paintings. The data are some of the width of the Chinese paintings in history. The the width of the Chinese paintings are quantitative.
Show:
chinese_art <-read.csv("artdata.cleaned.csv") %>%filter(country =="Chinese")ggplot(chinese_art, aes(x = width)) +geom_histogram(binwidth =10, color ="black") +labs(title ="width of the Chinese Paintings, Histogram",x ="width",y ="Count" ) +theme_minimal()
Warning: Removed 71 rows containing non-finite outside the scale range
(`stat_bin()`).
Tell: The mean an median are not close, so the outlier should be considered. Most Width is under 100. The number of big paintings are relative small.
2d. Thinking about your results
Consider the results of 2b. and 2c. together. What can we understand about Chinese art from viewing the distribution of these two variables? Both height and width are mainly under 100cm. From the graph the median of height is larger than width. The maximum of width is larger than height. Maybe a big painting, whose length and width are larger than 100, its width is more likely longer than height.
Question 3: Relationships between categorical variables - American and Chinese artists and oil vs. ink. (15 points)
3a. Recoding your data
Using the mutate() verb and the case_when() verb combined with grepl(), create two new variables. The first is material.type and the second is us.china. The first variable should recode material to be either Oil, Ink, or Other, depending on whether the original values of material contained either the words oil or ink. The second variable should make a similar transformation to country where you recode the variable to be either American, Chinese, or Other. Show the code you used to make the new variables using the #| echo: true code block option.
Hint 1: you can see some examples of case_when() and grepl()hereand here .
Hint 2: make sure to use the ignore.case=TRUE option in grepl()
3b. Investigating the categorical relationship between us.china and material.type
Investigate the relationship between us.china and material.type
Hint 3: you can see an example of some ways to display this information here
Think: I want to investigate the I want to investigate the distribution of the width of the Chinese paintings.
Show:
ggplot(chinese_art, aes(x = us.china, fill = material.type)) +geom_bar(position ="fill") +labs(title ="Relationship between us.china and material.type",x ="us.china",y ="Proportion" )
Tell: ink is more popular in paintings from the US and China. ink nearly 70%, more than a half. the proportion of oil and other are similar. oil is the second popular material.
3c.Thinking about your results
Think carefully about why you have observed this result and provide some additional information about what this investigation means for understanding this dataset and art in general. Oil painting techniques began to spread to China and the United States around the 18th and 19th centuries. Traditional Chinese ink painting (water-ink painting) has a long history. By the 19th century, advancements in the production of oil painting materials, such as oil paints and canvases, made oil painting more accessible to artists. ## Question 4: Comparing groups (15 points)
4a. Recoding your data
Similar to the previous question, create a new variable called famous.countries that recodes country to be either American, French, Italian and Spanish. Mark art from all other countries as NA (the code that stands for missing or not available in R). Additionally, create a new variable called area that is a calculation of the area of the art (height times width). Show the code you used to make the new variables using the #| echo: true code block option. library(dplyr)
4b. Compare the groups of countries on the variable price
Think I want to investigate the groups of countries on the variable price The data are price of paintings The the price of paintings paintings are quantitative.
Show:
library(ggplot2)library(dplyr)chinese_art <-read.csv("artdata.cleaned.csv")chinese_art <- chinese_art %>%mutate(famous.countries =case_when(grepl("america", country, ignore.case =TRUE) ~"American",grepl("france", country, ignore.case =TRUE) ~"French",grepl("italy", country, ignore.case =TRUE) ~"Italian",grepl("spain", country, ignore.case =TRUE) ~"Spanish",TRUE~NA_character_ ),area = height * width )chinese_art$price <-as.numeric(as.character(chinese_art$price))ggplot(chinese_art, aes(x = famous.countries, y = price, fill = famous.countries)) +geom_boxplot() +labs(title ="Comparison of Art Prices Across Different Countries",x ="Country Group",y ="Price" )
Tell: In American price of paintings are mainly under 3.75e+07. In NA price of paintings are mainly under 4.2e+07. Hishest price is not from American
4c. Compare the groups of countries on the variable area
Think : I want to investigate the groups of countries on the variable area The data are area of paintings The the area of paintings paintings are quantitative.
Show:
library(ggplot2)library(dplyr)chinese_art <-read.csv("artdata.cleaned.csv")chinese_art <- chinese_art %>%mutate(famous.countries =case_when(grepl("america", country, ignore.case =TRUE) ~"American",grepl("france", country, ignore.case =TRUE) ~"French",grepl("italy", country, ignore.case =TRUE) ~"Italian",grepl("spain", country, ignore.case =TRUE) ~"Spanish",TRUE~NA_character_ ),area = height * width )ggplot(chinese_art, aes(x = famous.countries, y = area, fill = famous.countries)) +geom_boxplot() +labs(title ="Comparison of Art Areas Across Different Countries",x ="Country Group",y ="Area" ) +scale_fill_brewer(palette ="Set3")
Warning: Removed 2419 rows containing non-finite outside the scale range
(`stat_boxplot()`).
Tell: The distribution of area is extreme. From the grahp, the paintings have really big area in American.
4d. Thinking about your results
Consider the results of 4b. and 4c. together. What can we learn about the differences in art between the countries? What do you think causes these differences or similarities? How would you confirm your guess as to the cause of the differences/similarities? Paintings’ price and area are different in different countries, but the difference is not big. The price of paintings are mainly under 4.2e+07. I think the similarities are beacuse art can be appreciate by everyone.
Question 5: Considering deviations (10 points)
5a. Selecting your data
Pick three years of paintings to investigate whether the brightness of paintings has changed over time. You are free to pick any three years but you should pick years that correspond to different periods in art history. State the three years and justify your selection. choose 1800 1850 1900
5b. Finding the average
Calculate the average brightness for each of the three years. Show your code using the #| echo: true code block option.
Tell: the absolute value of z_score is largest in 1850, is smallest in 1800. So in 1800 the observation is not far from the average value of the distribution. In 1850 the observation is further from the average value of the distribution.
5d. Thinking about your results
What are some of the implications of your findings with regard to the motivation of this question? What are some of the limitations of this analysis? What other kind of analysis would you like to do to answer this question? Implications:artistic preferences for brightness have changed over time. If certain periods show consistently higher or lower brightness levels, it might indicate a preference for lighter or darker themes. Limitations: only choose there period. Other analysis: how brightness affect the price of a painting. ## Question 6: Your own investigation (15 points)
6a. Selecting your own question
Similar to the previous questions, think of your own question that you would like to ask of the data. Use the Think-Show-Tell procedure to conduct your investigation. Think deeply about what your result means.
Think I want to find how many \(z\) units each of the averages for the years are away from the overall mean of highbrightness. use a table.
Tell: the absolute value of z_score is largest in 1800, is smallest in 1850. So in 1850 the observation is not far from the average value of the distribution. In 1800 the observation is further from the average value of the distribution.
6b. In summary
Sum up everything that you have learned in this investigation. Do not simply repeat/rephrase your previous results but try to say something larger that synthesizes the results together to draw a more meaningful general conclusion. Paintings have difference around the world. But the size and price do not have larger difference. Art have similarities in the world.
